Statistical power and significance testing in large-scale genetic studies

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works

PLINK: a tool set for whole-genome association and population-based linkage analyses.

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works

A method and server for predicting damaging missense mutations.

Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194 · see citing works

Statistical Methods for Research Workers

Fisher, R. A. (1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,883 · see citing works

Principal components analysis corrects for stratification in genome-wide association studies

Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248 · see citing works

Bootstrap Methods and their Application

Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350 · see citing works

A haplotype map of the human genome

The International HapMap Consortium (2005) · Nature · doi:10.1038/nature04226 · Cited by 5,858 · see citing works

Bootstrap Methods and Their Application

Loughin, T. M., Davison, A. C., & HINKLEY, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,478 · see citing works

Rectangular Confidence Regions for the Means of Multivariate Normal Distributions

Šidák, Z. (1967) · Journal of the American Statistical Association · doi:10.1080/01621459.1967.10482935 · Cited by 2,904 · see citing works

Genome-wide association studies for complex traits: consensus, uncertainty and challenges

McCarthy, M. I., Abecasis, G. R., Cardon, L. R., Goldstein, D. B., Little, J., Ioannidis, J. P. A., & Hirschhorn, J. N. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2344 · Cited by 2,891 · see citing works

Genome-wide association studies for common diseases and complex traits

Hirschhorn, J. N., & Daly, M. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1521 · Cited by 2,853 · see citing works

Five Years of GWAS Discovery

Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425 · see citing works

IX. On the problem of the most efficient tests of statistical hypotheses

Neyman, J., & Pearson, E. S. (1933) · Philosophical Transactions of the Royal Society of London Series A Containing Papers of a Mathematical or Physical Character · doi:10.1098/rsta.1933.0009 · Cited by 2,370 · see citing works

Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test

Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.05.029 · Cited by 2,317 · see citing works

Statistical Methods for Research Workers.

I., L., & Fisher, R. A. (1926) · Journal Of The Royal Statistical Society · doi:10.2307/2341488 · Cited by 2,171 · see citing works

Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits

Purcell, S., Cherny, S. S., & Sham, P. C. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.1.149 · Cited by 2,057 · see citing works

Functional impact of global rare copy number variation in autism spectrum disorders

Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., Regan, R., Conroy, J., Magalhaes, T. R., Correia, C., Abrahams, B. S., et al. (2010) · Nature · doi:10.1038/nature09146 · Cited by 2,013 · see citing works

Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies

Wacholder, S., Chanock, S. J., García‐Closas, M., ghormli, L. K. E., & Rothman, N. (2004) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djh075 · Cited by 1,743 · see citing works

A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other

Nyholt, D. R. (2004) · The American Journal of Human Genetics · doi:10.1086/383251 · Cited by 1,699 · see citing works

A comprehensive review of genetic association studies

Hirschhorn, J. N., Lohmueller, K., Byrne, E., & Hirschhorn, K. (2002) · Genetics in Medicine · doi:10.1097/00125817-200203000-00002 · Cited by 1,674 · see citing works

Exome sequencing as a tool for Mendelian disease gene discovery

Bamshad, M. J., Ng, S. B. H., Bigham, A. W., Tabor, H. K., Emond, M. J., Nickerson, D. A., & Shendure, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3031 · Cited by 1,659 · see citing works

ON THE PROBLEM OF THE MOST EFFICIENT TESTS OF STATISTICAL HYPOTHESES

Neyman, J., & Pearson, E. S. (1967) · University of California Press eBooks · doi:10.1525/9780520339897-008 · Cited by 1,545 · see citing works

Methods for Detecting Associations with Rare Variants for Common Diseases: Application to Analysis of Sequence Data

Li, B., & Leal, S. M. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.06.024 · Cited by 1,527 · see citing works

Why Most Discovered True Associations Are Inflated

Ioannidis, J. P. A. (2008) · Epidemiology · doi:10.1097/ede.0b013e31818131e7 · Cited by 1,495 · see citing works

Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix

Li, J., & Ji, L. (2005) · Heredity · doi:10.1038/sj.hdy.6800717 · Cited by 1,458 · see citing works

De Novo Gene Disruptions in Children on the Autistic Spectrum

Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y.-H., Narzisi, G., Leotta, A., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.009 · Cited by 1,446 · see citing works

Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome

Ng, S. B. H., Bigham, A. W., Buckingham, K. J., Hannibal, M., McMillin, M. J., Gildersleeve, H. I. S., Beck, A. E., Tabor, H. K., Cooper, G. M., Mefford, H. C., et al. (2010) · Nature Genetics · doi:10.1038/ng.646 · Cited by 1,349 · see citing works

A tutorial on statistical methods for population association studies

Balding, D. J. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1916 · Cited by 1,328 · see citing works

Genome-wide association studies: theoretical and practical concerns

Wang, W. Y. S., Barratt, B. J., Clayton, D. G., & Todd, J. A. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1522 · Cited by 1,159 · see citing works

A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic

Madsen, B. E., & Browning, S. R. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000384 · Cited by 1,113 · see citing works

Null hypothesis significance testing: a review of an old and continuing controversy

Nickerson, R. S. (2000) · Psychological Methods · doi:10.1037/1082-989x.5.2.241 · Cited by 1,090 · see citing works

Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets

Li, M.-X., Yeung, J. M. Y., Cherny, S. S., & Sham, P. C. (2012) · Human Genetics · doi:10.1007/s00439-011-1118-2 · Cited by 962 · see citing works

Rare Variants of <i>IFIH1</i> , a Gene Implicated in Antiviral Responses, Protect Against Type 1 Diabetes

Nejentsev, S., Walker, N., Riches, D., Egholm, M., & Todd, J. A. (2009) · Science · doi:10.1126/science.1167728 · Cited by 958 · see citing works

Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants

Fu, W., O’Connor, T. D., Jun, G., Kang, H. M., Abecasis, G. R., Leal, S. M., Gabriel, S., Rieder, M. J., Altshuler, D., Shendure, J., et al. (2012) · Nature · doi:10.1038/nature11690 · Cited by 951 · see citing works

Estimation of the multiple testing burden for genomewide association studies of nearly all common variants

Pe'er, I., Yelensky, R., Altshuler, D., & Daly, M. J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20303 · Cited by 783 · see citing works

Pooled Association Tests for Rare Variants in Exon-Resequencing Studies

Price, A. L., Kryukov, G. V., de Bakker, P. I. W., Purcell, S. M., Staples, J., Wei, L.-J., & Sunyaev, S. R. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.04.005 · Cited by 755 · see citing works

Estimation of significance thresholds for genomewide association scans

Dudbridge, F., & Gusnanto, A. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20297 · Cited by 703 · see citing works

Optimal tests for rare variant effects in sequencing association studies

Lee, S., Wu, M. C., & Lin, X. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs014 · Cited by 683 · see citing works

An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people

Nelson, M. R., Wegmann, D., Ehm, M. G., Kessner, D., St. Jean, P., Verzilli, C., Shen, J., Tang, Z., Bacanu, S.-A., Fraser, D., et al. (2012) · Science · doi:10.1126/science.1217876 · Cited by 647 · see citing works

Sample size requirements for matched case‐control studies of gene–environment interaction

Gauderman, W. J. (2001) · Statistics in Medicine · doi:10.1002/sim.973 · Cited by 643 · see citing works

Searching for missing heritability: Designing rare variant association studies

Zuk, O., Schaffner, S. F., Samocha, K., Do, R., Hechter, E., Kathiresan, S., Daly, M. J., Neale, B. M., Sunyaev, S. R., & Lander, E. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322563111 · Cited by 635 · see citing works

Sample Size Requirements for Association Studies of Gene-Gene Interaction

Gauderman, W. J. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.5.478 · Cited by 615 · see citing works

Most Rare Missense Alleles Are Deleterious in Humans: Implications for Complex Disease and Association Studies

Kryukov, G. V., Pennacchio, L. A., & Sunyaev, S. R. (2007) · The American Journal of Human Genetics · doi:10.1086/513473 · Cited by 598 · see citing works

The Non-Central χ 2 - and F-Distribution and their Applications

PATNAIK, P. B. (1949) · Biometrika · doi:10.2307/2332542 · Cited by 598 · see citing works

Testing for an Unusual Distribution of Rare Variants

Neale, B. M., Rivas, M. A., Voight, B. F., Altshuler, D., Devlin, B., Orho‐Melander, M., Kathiresan, S., Purcell, S., Roeder, K., & Daly, M. J. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001322 · Cited by 583 · see citing works

The non-central chi2- and F-distributions and their applications.

PATNAIK, P. B. (1949) · PubMed · Cited by 556 · see citing works

Bayesian statistical methods for genetic association studies

Stephens, M., & Balding, D. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2615 · Cited by 465 · see citing works

The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits

Voight, B. F., Kang, H. M., Ding, J., Palmer, C. D., Sidore, C., Chines, P. S., Burtt, N. P., Fuchsberger, C., Li, Y., Erdmann, J., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002793 · Cited by 464 · see citing works

Statistical analysis strategies for association studies involving rare variants

Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2867 · Cited by 455 · see citing works

Combined Linkage and Association Sib-Pair Analysis for Quantitative Traits

Fulker, D. W., Cherny, S. S., Sham, P. C., & Hewitt, J. K. (1999) · The American Journal of Human Genetics · doi:10.1086/302193 · Cited by 443 · see citing works

Analysis of copy number variations at 15 schizophrenia-associated loci

Rees, E., Walters, J. T. R., Georgieva, L., Isles, A. R., Chambert, K. D., Richards, A. L., Mahoney-Davies, G., Legge, S. E., Moran, J. L., McCarroll, S. A., et al. (2014) · British Journal of Psychiatry · doi:10.1192/bjp.bp.113.131052 · Cited by 443 · see citing works

A Note on the Calculation of Empirical P Values from Monte Carlo Procedures

North, B. V., Curtis, D., & Sham, P. C. (2002) · The American Journal of Human Genetics · doi:10.1086/341527 · Cited by 426 · see citing works

Bayes factors for genome‐wide association studies: comparison with P ‐values

Wakefield, J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20359 · Cited by 397 · see citing works

Differential confounding of rare and common variants in spatially structured populations

Mathieson, I., & McVean, G. (2012) · Nature Genetics · doi:10.1038/ng.1074 · Cited by 385 · see citing works

Overcoming the Winner’s Curse: Estimating Penetrance Parameters from Case-Control Data

Zöllner, S. K., & Pritchard, J. K. (2007) · The American Journal of Human Genetics · doi:10.1086/512821 · Cited by 382 · see citing works

Exome sequencing and the genetic basis of complex traits

Kiezun, A., Garimella, K., Do, R., Stitziel, N. O., Neale, B. M., McLaren, P. J., Gupta, N., Sklar, P., Sullivan, P. F., Moran, J. L., et al. (2012) · Nature Genetics · doi:10.1038/ng.2303 · Cited by 372 · see citing works

Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies

Duggal, P., Gillanders, E. M., Holmes, T. N., & Wilson, J. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-516 · Cited by 330 · see citing works

Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes

He, X., Sanders, S. J., Liu, L., De Rubeis, S., Lim, E. T., Sutcliffe, J. S., Schellenberg, G. D., Gibbs, R. A., Daly, M. J., Buxbaum, J. D., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003671 · Cited by 304 · see citing works

Genetic associations: false or true?

Ioannidis, J. P. A. (2003) · Trends in Molecular Medicine · doi:10.1016/s1471-4914(03)00030-3 · Cited by 289 · see citing works

What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations

Panagiotou, O. A., Ioannidis, J. P. A., & for the Genome-Wide Significance Project (2012) · International Journal of Epidemiology · doi:10.1093/ije/dyr178 · Cited by 286 · see citing works

A General Framework for Detecting Disease Associations with Rare Variants in Sequencing Studies

Lin, D.-Y., & Tang, Z.-Z. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.07.015 · Cited by 270 · see citing works

On multiple‐testing correction in genome‐wide association studies

Moskvina, V., & Schmidt, K. M. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20331 · Cited by 266 · see citing works

Rare Complete Knockouts in Humans: Population Distribution and Significant Role in Autism Spectrum Disorders

Lim, E. T., Raychaudhuri, S., Sanders, S. J., Stevens, C., Sabo, A., MacArthur, D. G., Neale, B. M., Kirby, A., Ruderfer, D. M., Fromer, M., et al. (2013) · Neuron · doi:10.1016/j.neuron.2012.12.029 · Cited by 266 · see citing works

Power of Linkage versus Association Analysis of Quantitative Traits, by Use of Variance-Components Models, for Sibship Data

Sham, P. C., Cherny, S. S., Purcell, S., & Hewitt, J. K. (2000) · The American Journal of Human Genetics · doi:10.1086/302891 · Cited by 260 · see citing works

A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases

Li, M., Gui, H., Kwan, J. S. H., Bao, S., & Sham, P. C. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1257 · Cited by 247 · see citing works

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

Lee, S., Teslovich, T. M., Boehnke, M., & Lin, X. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.05.010 · Cited by 218 · see citing works

Comparison of statistical tests for disease association with rare variants

Basu, S., & Pan, W. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20609 · Cited by 215 · see citing works

Genome‐wide significance for dense SNP and resequencing data

Hoggart, C. J., Clark, T. G., Iorio, M. D., Whittaker, J. C., & Balding, D. J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20292 · Cited by 209 · see citing works

A new measure of the effective number of tests, a practical tool for comparing families of non‐independent significance tests

Galwey, N. W. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20408 · Cited by 189 · see citing works

Efficient Computation of Significance Levels for Multiple Associations in Large Studies of Correlated Data, Including Genomewide Association Studies

Dudbridge, F., & Koeleman, B. P. C. (2004) · The American Journal of Human Genetics · doi:10.1086/423738 · Cited by 166 · see citing works

Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies

Zhong, H., & Prentice, R. L. (2008) · Biostatistics · doi:10.1093/biostatistics/kxn001 · Cited by 161 · see citing works

Immunochip analyses identify a novel risk locus for primary biliary cirrhosis at 13q14, multiple independent associations at four established risk loci and epistasis between 1p31 and 7q32 risk variants

Juran, B. D., Hirschfield, G. M., Invernizzi, P., Atkinson, E. J., Li, Y., Xie, G., Kosoy, R., Ransom, M., Sun, Y., Bianchi, I., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds359 · Cited by 150 · see citing works

Discovery of Rare Variants via Sequencing: Implications for the Design of Complex Trait Association Studies

Li, B., & Leal, S. M. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000481 · Cited by 142 · see citing works

Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies

Li, D., Lewinger, J. P., Gauderman, W. J., Murcray, C. E., & Conti, D. V. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20628 · Cited by 140 · see citing works

Computational and statistical approaches to analyzing variants identified by exome sequencing

Stitziel, N. O., Kieżun, A., & Sunyaev, S. R. (2011) · Genome biology · doi:10.1186/gb-2011-12-9-227 · Cited by 137 · see citing works

Analysis of Rare, Exonic Variation amongst Subjects with Autism Spectrum Disorders and Population Controls

Liu, L., Sabo, A., Neale, B. M., Nagaswamy, U., Stevens, C., Lim, E., Bodea, C. A., Muzny, D., Reid, J. G., Banks, E., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003443 · Cited by 135 · see citing works

Sample size determination for studies of gene-environment interaction

Luan, J., Wong, M., Day, N., & Wareham, N. (2001) · International Journal of Epidemiology · doi:10.1093/ije/30.5.1035 · Cited by 123 · see citing works

Maximizing association statistics over genetic models

González, J. R., Carrasco, J. L., Dudbridge, F., Armengol, L., Estivill, X., & Moreno, V. R. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20299 · Cited by 122 · see citing works

Including known covariates can reduce power to detect genetic effects in case-control studies

Pirinen, M., Donnelly, P., & Spencer, C. C. A. (2012) · Nature Genetics · doi:10.1038/ng.2346 · Cited by 117 · see citing works

Linkage Analysis in the Next-Generation Sequencing Era

Bailey-Wilson, J., & Wilson, A. F. (2011) · Human Heredity · doi:10.1159/000334381 · Cited by 112 · see citing works

The Empirical Power of Rare Variant Association Methods: Results from Sanger Sequencing in 1,998 Individuals

Ladouceur, M., Dastani, Z., Aulchenko, Y. S., Greenwood, C. M. T., & Richards, J. B. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002496 · Cited by 107 · see citing works

Rapid Simulation of P Values for Product Methods and Multiple-Testing Adjustment in Association Studies

Seaman, S. R., & Müller‐Myhsok, B. (2005) · The American Journal of Human Genetics · doi:10.1086/428140 · Cited by 106 · see citing works

Family-based association tests for sequence data, and comparisons with population-based association tests

Ionita‐Laza, I., Lee, S., Makarov, V., Buxbaum, J. D., & Lin, X. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.308 · Cited by 87 · see citing works

A Note on the Calculation of Empirical P Values from Monte Carlo Procedures

North, B. V., Curtis, D., & Sham, P. C. (2003) · The American Journal of Human Genetics · doi:10.1086/346173 · Cited by 75 · see citing works

Efficient Approximation of P‐value of the Maximum of Correlated Tests, with Applications to Genome‐Wide Association Studies

Li, Q., Zheng, G., Li, Z., & Yu, K. F. (2008) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2008.00437.x · Cited by 74 · see citing works

Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates

So, H., & Sham, P. C. (2011) · Behavior Genetics · doi:10.1007/s10519-011-9450-9 · Cited by 73 · see citing works

Estimating Odds Ratios in Genome Scans: An Approximate Conditional Likelihood Approach

Ghosh, A., Zou, F., & Wright, F. A. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.03.002 · Cited by 66 · see citing works

Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics

Hu, Y., Berndt, S. I., Gustafsson, S., Ganna, A., Berndt, S. I., Gustafsson, S., Mägi, R., Ganna, A., Wheeler, E., Feitosa, M. F., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.06.011 · Cited by 64 · see citing works

Replication Strategies for Rare Variant Complex Trait Association Studies via Next-Generation Sequencing

Liu, D. J., & Leal, S. M. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.10.025 · Cited by 58 · see citing works

How Many Genetic Variants Remain to Be Discovered?

Yudi Pawitan, Y., Seng, K. C., & Magnusson, P. K. E. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007969 · Cited by 46 · see citing works

Pooled Association Tests for Rare Variants in Exon-Resequencing Studies

Price, A. L., Kryukov, G. V., de Bakker, P. I. W., Purcell, S., Staples, J., Wei, L., & Sunyaev, S. R. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.05.014 · Cited by 43 · see citing works

THE POWER FUNCTION OF THE TEST FOR THE DIFFERENCE BETWEEN TWO PROPORTIONS IN A 2×2 TABLE

PATNAIK, P. B. (1948) · Biometrika · doi:10.1093/biomet/35.1-2.157 · Cited by 39 · see citing works

Power Comparisons of the Transmission/Disequilibrium Test and Sib–Transmission/Disequilibrium-Test Statistics

Whittaker, J. C., & Lewis, C. M. (1999) · The American Journal of Human Genetics · doi:10.1086/302485 · Cited by 32 · see citing works

Statistical Guidance for Experimental Design and Data Analysis of Mutation Detection in Rare Monogenic Mendelian Diseases by Exome Sequencing

Zhi, D., & Chen, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031358 · Cited by 27 · see citing works

SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits

Li, B., Wang, G. T., & Leal, S. M. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts499 · Cited by 21 · see citing works

Design Considerations for Massively Parallel Sequencing Studies of Complex Human Disease

Feng, B., Tavtigian, S. V., Southey, M. C., & Goldgar, D. E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023221 · Cited by 20 · see citing works

Empirical power of very rare variants for common traits and disease: results from sanger sequencing 1998 individuals

Ladouceur, M., Zheng, H., Greenwood, C. M. T., & Richards, J. B. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.284 · Cited by 12 · see citing works

Three Ways of Combining Genotyping and Resequencing in Case-Control Association Studies

Longmate, J., Larson, G. P., Krontiris, T. G., & Sommer, S. S. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014318 · Cited by 9 · see citing works

Combining effects from rare and common genetic variants in an exome-wide association study of sequence data

Aschard, H., Qiu, W., Paşaniuc, B., Zaitlen, N., Cho, M. H., & Carey, V. J. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s44 · Cited by 8 · see citing works

Comparative study of statistical methods for detecting association with rare variants in exome-resequencing data

Saad, M., Pierre, A. S., Bohossian, N., Macé, M., & Martinez, M. M. (2011) · BMC Proceedings · doi:10.1186/1753-6561-5-s9-s33 · Cited by 5 · see citing works

Novel Sib Pair Selection Strategy Increases Power in Quantitative Association Analysis

Kwan, J. S. H., Cherny, S. S., Kung, A. W. C., & Sham, P. C. (2009) · Behavior Genetics · doi:10.1007/s10519-009-9284-x · Cited by 4 · see citing works

Detecting multiple causal rare variants in exome sequence data

Ye, K. Q., & Engelman, C. D. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20644 · Cited by 4 · see citing works

A global reference for human genetic variation

The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

GERAD1 Consortium, Pardiñas, A. F., CRESTAR Consortium, Holmans, P., Pocklington, A. J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S. E., Bishop, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0059-2 · Cited by 1,552 · see citing works

All for One and One for All: Mental Disorders in One Dimension

Caspi, A., & Moffitt, T. E. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.17121383 · Cited by 918 · see citing works

Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions

Hoban, S., Kelley, J. L., Lotterhos, K. E., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016) · The American Naturalist · doi:10.1086/688018 · Cited by 876 · see citing works

Advancing the understanding of autism disease mechanisms through genetics

de la Torre-Ubieta, L., Won, H., Stein, J. L., & Geschwind, D. H. (2016) · Nature Medicine · doi:10.1038/nm.4071 · Cited by 827 · see citing works

Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource

Strande, N. T., Riggs, E. R., Buchanan, A. H., Ceyhan‐Birsoy, O., DiStefano, M. T., Dwight, S. S., Goldstein, J., Ghosh, R., Seifert, B. A., Sneddon, T. P., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.015 · Cited by 587 · see citing works

Large-scale plasma proteomics comparisons through genetics and disease associations

Eldjarn, G. H., Ferkingstad, E., Lund, S. H., Helgason, H., Magnusson, O. T., Gunnarsdottir, K., Olafsdottir, T. A., Halldorsson, B. V., Olason, P. I., Zink, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06563-x · Cited by 583 · see citing works

Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach

Leo, G. D., & Sardanelli, F. (2020) · European Radiology Experimental · doi:10.1186/s41747-020-0145-y · Cited by 489 · see citing works

Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

Purcell, S. M., Manoach, D. S., Demanuele, C., Cade, B. E., Mariani, S., Cox, R., Panagiotaropoulou, G., Saxena, R., Pan, J. Q., Smoller, J. W., et al. (2017) · Nature Communications · doi:10.1038/ncomms15930 · Cited by 439 · see citing works

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works

Genome-wide association studies of cancer: current insights and future perspectives

Sud, A., Kinnersley, B., & Houlston, R. S. (2017) · Nature reviews. Cancer · doi:10.1038/nrc.2017.82 · Cited by 383 · see citing works

A population genetic interpretation of GWAS findings for human quantitative traits

Simons, Y. B., Bullaughey, K., Hudson, R. R., & Sella, G. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2002985 · Cited by 363 · see citing works

The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants

Fadista, J., Manning, A. K., Florez, J. C., & Groop, L. (2016) · European Journal of Human Genetics · doi:10.1038/ejhg.2015.269 · Cited by 357 · see citing works

CHCHD2 mutations in autosomal dominant late-onset Parkinson's disease: a genome-wide linkage and sequencing study

Funayama, M., Ohe, K., Amo, T., Furuya, N., Yamaguchi, J., Saiki, S., Li, Y., Ogaki, K., Ando, M., Yoshino, H., et al. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70266-2 · Cited by 351 · see citing works

Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Christophersen, I. E., Rienstra, M., Roselli, C., Yin, X., Geelhoed, B., Barnard, J., Lin, H., Arking, D. E., Smith, A. V., Albert, C. M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3843 · Cited by 339 · see citing works

Parkinson's disease age at onset genome‐wide association study: Defining heritability, genetic loci, and α‐synuclein mechanisms

Blauwendraat, C., Heilbron, K., Vallerga, C. L., Bandrés‐Ciga, S., von Coelln, R., Pihlstrøm, L., Simón‐Sánchez, J., Schulte, C., Sharma, M., Krohn, L., et al. (2019) · Movement Disorders · doi:10.1002/mds.27659 · Cited by 326 · see citing works

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Moss, D. J. H., Pardiñas, A. F., Langbehn, D., Lo, K., Leavitt, B. R., Roos, R., Durr, A., Mead, S., Coleman, A., Santos, R. D., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30161-8 · Cited by 312 · see citing works

Thinking About the Evolution of Complex Traits in the Era of Genome-Wide Association Studies

Sella, G., & Barton, N. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022316 · Cited by 263 · see citing works

Rare variant association studies: considerations, challenges and opportunities

Auer, P. L., & Lettre, G. (2015) · Genome Medicine · doi:10.1186/s13073-015-0138-2 · Cited by 246 · see citing works

On the reproductive success of early‐generation hatchery fish in the wild

Christie, M. R., Ford, M. J., & Blouin, M. S. (2014) · Evolutionary Applications · doi:10.1111/eva.12183 · Cited by 238 · see citing works

The road to precision psychiatry: translating genetics into disease mechanisms

Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4409 · Cited by 223 · see citing works

Rare-variant collapsing analyses for complex traits: guidelines and applications

Povysil, G., Petrovski, S., Hostyk, J. A., Aggarwal, V. S., Allen, A. S., & Goldstein, D. B. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0177-4 · Cited by 202 · see citing works

GWAS for systemic sclerosis identifies multiple risk loci and highlights fibrotic and vasculopathy pathways

López‐Isac, E., Acosta‐Herrera, M., Kerick, M., Assassi, S., Satpathy, A. T., Granja, J. M., Mumbach, M. R., Beretta, L., Simeón‐Aznar, C. P., Carreira, P. E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12760-y · Cited by 182 · see citing works

A Large-Scale Genetic Analysis Reveals a Strong Contribution of the HLA Class II Region to Giant Cell Arteritis Susceptibility

Carmona, F. D., Mackie, S. L., Martín, J., Taylor, J., Vaglio, A., Eyre, S., Bossini‐Castillo, L., Castañeda, S., Cid, M. C., Hernández‐Rodríguez, J., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.02.009 · Cited by 177 · see citing works

Weighting sequence variants based on their annotation increases power of whole-genome association studies

Sveinbjörnsson, G., Albrechtsen, A., Zink, F., Gudjonsson, S. A., Oddsson, Á., Másson, G., Hólm, H., Kong, A., Þorsteinsdóttir, U., Sulem, P., et al. (2016) · Nature Genetics · doi:10.1038/ng.3507 · Cited by 175 · see citing works

Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium

Stringer, S., Minică, C. C., Verweij, K. J. H., Mbarek, H., Bernard, M., Derringer, J. L., van Eijk, K. R., Isen, J. D., Loukola, A., Maciejewski, D. F., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.36 · Cited by 158 · see citing works

Precision radiology: predicting longevity using feature engineering and deep learning methods in a radiomics framework

Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J. C., Bradley, A. P., & Palmer, L. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-01931-w · Cited by 154 · see citing works

Inherited genetic variation in childhood acute lymphoblastic leukemia

Moriyama, T., Relling, M. V., & Yang, J. J. J. (2015) · Blood · doi:10.1182/blood-2014-12-580001 · Cited by 149 · see citing works

Facial Genetics: A Brief Overview

Richmond, S., Howe, L. J., Lewis, S. J., Stergiakouli, E., & Zhurov, A. I. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00462 · Cited by 141 · see citing works

Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry

So, H., Chau, C. K., Chiu, W.-T., Ho, K.-S., Lo, C.-P., Yim, S. H.-Y., & Sham, P. C. (2017) · Nature Neuroscience · doi:10.1038/nn.4618 · Cited by 139 · see citing works

Association between a common immunoglobulin heavy chain allele and rheumatic heart disease risk in Oceania

Parks, T., Mirabel, M., Kado, J. H., Auckland, K., Nowak, J., Rautanen, A., Mentzer, A. J., Marijon, É., Jouven, X., Perman, M. L., et al. (2017) · Nature Communications · doi:10.1038/ncomms14946 · Cited by 138 · see citing works

ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Silva, T. C., Coetzee, S. G., Gull, N., Yao, L., Hazelett, D. J., Noushmehr, H., Lin, D., & Berman, B. P. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty902 · Cited by 131 · see citing works

Gene × Environment Determinants of Stress- and Anxiety-Related Disorders

Sharma, S. K., Powers, A. D., Bradley, B., & Ressler, K. J. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033408 · Cited by 127 · see citing works

Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology

Read, T. D., & Massey, R. C. (2014) · Genome Medicine · doi:10.1186/s13073-014-0109-z · Cited by 125 · see citing works

Sex differences in disease genetics: evidence, evolution, and detection

Gilks, W. P., Abbott, J. K., & Morrow, E. H. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.08.006 · Cited by 123 · see citing works

Coselected genes determine adaptive variation in herbivore resistance throughout the native range of Arabidopsis thaliana

Brachi, B., Meyer, C. G., Villoutreix, R., Platt, A., Morton, T. C., Roux, F., & Bergelson, J. M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421416112 · Cited by 119 · see citing works

Multitrait genome‐wide association analysis of Populus trichocarpa identifies key polymorphisms controlling morphological and physiological traits

Chhetri, H. B., Macaya‐Sanz, D., Kainer, D., Biswal, A. K., Evans, L. M., Chen, J., Collins, C., Hunt, K., Mohanty, S. S., Rosenstiel, T. N., et al. (2019) · New Phytologist · doi:10.1111/nph.15777 · Cited by 119 · see citing works

Variants in saposin D domain of prosaposin gene linked to Parkinson’s disease

Oji, Y., Hatano, T., Ueno, S.-I., Funayama, M., Ishikawa, K.-I., Okuzumi, A., Noda, S., Sato, S., Satake, W., Toda, T., et al. (2020) · Brain · doi:10.1093/brain/awaa064 · Cited by 116 · see citing works

Genetics of neurodegenerative diseases: an overview

Pihlstrøm, L., Wiethoff, S., & Houlden, H. (2017) · Handbook of clinical neurology · doi:10.1016/b978-0-12-802395-2.00022-5 · Cited by 116 · see citing works

Genetic diversity, linkage disequilibrium and power of a large grapevine (Vitis vinifera L) diversity panel newly designed for association studies

Nicolas, S., Péros, J., Lacombe, T., Launay, A., Paslier, M. L., Berard, A., Mangin, B., Valière, S., Martins, F., Cunff, L. L., et al. (2016) · BMC Plant Biology · doi:10.1186/s12870-016-0754-z · Cited by 112 · see citing works

Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies

Palmer, C. D., & Pe’er, I. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006916 · Cited by 112 · see citing works

Strategies to design clinical studies to identify predictive biomarkers in cancer research

Perez‐Gracia, J. L., Sanmamed, M. F., Bosch, A., Patiño‐García, A., Schalper, K. A., Segura, V., Joaquim Bellmunt, B., Tabernero, J. M., Sweeney, C. J., Choueiri, T. K., et al. (2016) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2016.12.005 · Cited by 109 · see citing works

Human candidate gene polymorphisms and risk of severe malaria in children in Kilifi, Kenya: a case-control association study

Ndila, C. M., Uyoga, S., Macharia, A. W., Nyutu, G., Peshu, N., Ojal, J., Shebe, M., Awuondo, K. O., Mturi, N., Tsofa, B., et al. (2018) · The Lancet Haematology · doi:10.1016/s2352-3026(18)30107-8 · Cited by 108 · see citing works

Childhood gene-environment interactions and age-dependent effects of genetic variants associated with refractive error and myopia: The CREAM Consortium

Fan, Q., Guo, X., Tideman, J. W. L., Williams, K. M., Yazar, S., Hosseini, S. M., Howe, L. D., Pourcain, B. S., Evans, D. M., Timpson, N. J., et al. (2016) · Scientific Reports · doi:10.1038/srep25853 · Cited by 107 · see citing works

What can genome‐wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

Josephs, E. B., Stinchcombe, J. R., & Wright, S. (2017) · New Phytologist · doi:10.1111/nph.14410 · Cited by 107 · see citing works

Post-GWAS in prostate cancer: from genetic association to biological contribution

Farashi, S., Kryza, T., Clements, J. A., & Batra, J. (2018) · Nature reviews. Cancer · doi:10.1038/s41568-018-0087-3 · Cited by 104 · see citing works

Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes

Zhu, X., & Stephens, M. (2018) · Nature Communications · doi:10.1038/s41467-018-06805-x · Cited by 101 · see citing works

Reproducibility and replicability in zebrafish behavioral neuroscience research

Gerlai, R. T. (2018) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2018.02.005 · Cited by 100 · see citing works

A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle

Pausch, H., Emmerling, R., Schwarzenbacher, H., & Fries, R. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0190-4 · Cited by 99 · see citing works

Multimodal Brain Imaging Reveals Structural Differences in Alzheimer’s Disease Polygenic Risk Carriers: A Study in Healthy Young Adults

Foley, S. F., Tansey, K. E., Caseras, X., Lancaster, T. M., Bracht, T., Parker, G. D., Hall, J., Williams, J. H., & Linden, D. E. J. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.02.033 · Cited by 95 · see citing works

Genetic variants associated with antithyroid drug-induced agranulocytosis: a genome-wide association study in a European population

Hallberg, P., Eriksson, N., Ibáñez, L., Bondon‐Guitton, E., Kreutz, R. H., Carvajal, A., Lucena, M. I., Ponce, E. S., Molokhia, M., Martin, J. J., et al. (2016) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(16)00113-3 · Cited by 94 · see citing works

Discovering Health Benefits of Phytochemicals with Integrated Analysis of the Molecular Network, Chemical Properties and Ethnopharmacological Evidence

Yoo, S., Kim, K., Nam, H., & Lee, D. (2018) · Nutrients · doi:10.3390/nu10081042 · Cited by 93 · see citing works

Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution

Pausch, H., Emmerling, R., Gredler, B., Fries, R., Daetwyler, H. D., & Goddard, M. E. (2017) · BMC Genomics · doi:10.1186/s12864-017-4263-8 · Cited by 93 · see citing works

Common single nucleotide variants underlying drug addiction: more than a decade of research

Bühler, K., Giné, E., Echeverry‐Alzate, V., Calleja‐Conde, J., de Fonseca, F. R., & López‐Moreno, J. A. (2015) · Addiction Biology · doi:10.1111/adb.12204 · Cited by 88 · see citing works

Combined Treatment With Environmental Enrichment and (-)-Epigallocatechin-3-Gallate Ameliorates Learning Deficits and Hippocampal Alterations in a Mouse Model of Down Syndrome

Catuara‐Solarz, S., Espinosa‐Carrasco, J., Erb, I., Langohr, K., González, J. R., Notredame, C., & Dierssen, M. (2016) · eNeuro · doi:10.1523/eneuro.0103-16.2016 · Cited by 87 · see citing works

Combined genetic analysis of juvenile idiopathic arthritis clinical subtypes identifies novel risk loci, target genes and key regulatory mechanisms

López‐Isac, E., Smith, S. L., Marion, M. C., Wood, A., Sudman, M., Yarwood, A., Shi, C., Gaddi, V. P., Martin, P., Prahalad, S., et al. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-218481 · Cited by 87 · see citing works

Single nucleotide polymorphisms in clinics: Fantasy or reality for cancer?

Srinivasan, S., Clements, J. A., & Batra, J. (2015) · Critical Reviews in Clinical Laboratory Sciences · doi:10.3109/10408363.2015.1075469 · Cited by 85 · see citing works

Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification

Moore, C. M., Jacobson, S. A., & Fingerlin, T. E. (2019) · Human Heredity · doi:10.1159/000508558 · Cited by 84 · see citing works

HLA-DRB1*15:01 and HLA-DRB3*02:02 in PLA2R-Related Membranous Nephropathy

Le, W., Shi, J., Zhang, T., Liu, L., Qin, H.-Z., Liang, S., Zhang, Y., Zheng, C.-X., Jiang, S., Qin, W., et al. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016060644 · Cited by 80 · see citing works

A power fallacy

Wagenmakers, E., Verhagen, J., Ly, A., Bakker, M., Lee, M. D., Matzke, D., Rouder, J. N., & Morey, R. D. (2014) · Behavior Research Methods · doi:10.3758/s13428-014-0517-4 · Cited by 79 · see citing works

HLA‐associated antiepileptic drug‐induced cutaneous adverse reactions

Mullan, K. A., Anderson, A., Illing, P. T., Kwan, P., Purcell, A. W., & Mifsud, N. A. (2019) · HLA · doi:10.1111/tan.13530 · Cited by 79 · see citing works

Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids

Seymour, D. K., Chae, E., Grimm, D. G., Martín‐Pizarro, C., Habring, A., Vasseur, F., Rakitsch, B., Borgwardt, K. M., Koenig, D., & Weigel, D. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615268113 · Cited by 78 · see citing works

Analysis of Gene‐Gene Interactions

Cole, B. S., Hall, M. A., Urbanowicz, R. J., Gilbert‐Diamond, D., & Moore, J. H. (2017) · Current Protocols in Human Genetics · doi:10.1002/cphg.45 · Cited by 78 · see citing works

Neurology Individualized Medicine: When to Use Next-Generation Sequencing Panels

Klein, C. J., & Foroud, T. (2017) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.09.008 · Cited by 76 · see citing works

Pharmacogenomics of acute lymphoblastic leukemia

Kager, L., & Evans, W. E. (2006) · Current Opinion in Hematology · doi:10.1097/01.moh.0000231424.46148.f9 · Cited by 75 · see citing works

Genome-wide Approaches to Investigate Anthelmintic Resistance

Doyle, S. R., & Cotton, J. A. (2019) · Trends in Parasitology · doi:10.1016/j.pt.2019.01.004 · Cited by 74 · see citing works

Exploring human brain lateralization with molecular genetics and genomics

Francks, C. (2015) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.12770 · Cited by 73 · see citing works

Meta-analysis of genome-wide association studies for loin muscle area and loin muscle depth in two Duroc pig populations

Zhuang, Z., Li, S., Ding, R., Yang, M., Zheng, E., Yang, H., Gu, T., Xu, Z., Cai, G., Wu, Z., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218263 · Cited by 71 · see citing works

The virtuous cycle of human genetics and mouse models in drug discovery

Nadeau, J. H., & Auwerx, J. H. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-018-0009-9 · Cited by 71 · see citing works

Complement system biomarkers in first episode psychosis

Kopczynska, M., Zelek, W. M., Touchard, S., Gaughran, F., Forti, M. D., Mondelli, V., Murray, R. M., O’Donovan, M. C., & Morgan, B. P. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.12.012 · Cited by 70 · see citing works

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

Kao, P. Y. P., Leung, K. H., Chan, L., Yip, S. P., & Yap, M. K. H. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.11.030 · Cited by 69 · see citing works

Sequencing and imputation in GWAS: Cost‐effective strategies to increase power and genomic coverage across diverse populations

Quick, C., Anugu, P., Musani, S. K., Weiss, S. T., Burchard, E. G., White, M. J., Keys, K. L., Cucca, F., Sidore, C., Boehnke, M., et al. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22326 · Cited by 68 · see citing works

Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome

Schlauch, K. A., Khaiboullina, S. F., Meirleir, K. L. D., Rawat, S., Petereit, J., Rizvanov, A. A., Blatt, N. L., Mijatovic, T., Kulick, D., Palotás, A., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2015.208 · Cited by 68 · see citing works

Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference

Corbin, L. J., Tan, V. Y., Hughes, D. A., Wade, K. H., Paul, D. S., Tansey, K. E., Butcher, F. C., Dudbridge, F., Howson, J. M. M., Jallow, M. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03109-y · Cited by 66 · see citing works

Association between polygenic risk scores for attention-deficit hyperactivity disorder and educational and cognitive outcomes in the general population

Stergiakouli, E., Martin, J., Hamshere, M. L., Heron, J., Pourcain, B. S., Timpson, N. J., Thapar, A., & Smith, G. D. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw216 · Cited by 65 · see citing works

Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle

Rowan, T. N., Durbin, H. J., Seabury, C. M., Schnabel, R. D., & Decker, J. E. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009652 · Cited by 59 · see citing works

Genetic Complexity of Crohn’s Disease in Two Large Ashkenazi Jewish Families

Levine, A. P., Pontikos, N., Schiff, E., Jostins, L., Speed, D., Lovat, L. B., Barrett, J. C., Grasberger, H., Plagnol, V., & Segal, A. W. (2016) · Gastroenterology · doi:10.1053/j.gastro.2016.06.040 · Cited by 59 · see citing works

How Powerful is the Evidence in Criminology? On Whether We Should Fear a Coming Crisis of Confidence

Barnes, J. C., TenEyck, M. F., Pratt, T. C., & Cullen, F. T. (2019) · Justice Quarterly · doi:10.1080/07418825.2018.1495252 · Cited by 59 · see citing works

Barriers in a sea of elasmobranchs: Fromfishingfor populations to testing hypotheses in population genetics

Hirschfeld, M., Dudgeon, C. L., Sheaves, M. J., & Barnett, A. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13379 · Cited by 58 · see citing works

Root and shoot variation in relation to potential intermittent drought adaptation of Mesoamerican wild common bean (Phaseolus vulgaris L.)

y Terán, J. C. B. M., Konzen, E. R., Medina, V., Palkovic, A., Ariani, A., Tsai, S. M., Gilbert, M. E., & Gepts, P. (2018) · Annals of Botany · doi:10.1093/aob/mcy221 · Cited by 58 · see citing works

Bilingualism Is Associated with a Delayed Onset of Dementia but Not with a Lower Risk of Developing it: a Systematic Review with Meta-Analyses

Brini, S., Sohrabi, H., Hébert, J., Forrest, M., Laine, M., Hämäläinen, H., Karrasch, M., Peiffer, J., Martins, R., & Fairchild, T. (2020) · Neuropsychology Review · doi:10.1007/s11065-020-09426-8 · Cited by 53 · see citing works

Pandemrix-induced narcolepsy is associated with genes related to immunity and neuronal survival

Hallberg, P., Smedje, H., Eriksson, N., Kohnke, H., Daniilidou, M., Öhman, I., Yue, Q., Cavalli, M., Wadelius, C., Magnusson, P. K. E., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.01.041 · Cited by 56 · see citing works

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Sahana, G., Cai, Z., Sanchez, M.-P., Bouwman, A. C., & Boichard, D. (2023) · Journal of Dairy Science · doi:10.3168/jds.2022-22694 · Cited by 55 · see citing works

Genetics of Complex Traits in Psychiatry

Gelernter, J. E. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.08.005 · Cited by 55 · see citing works

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function

Li, H., Wang, X., Rukina, D., Huang, Q. D., Lin, T., Sorrentino, V., Zhang, H., Sleiman, M. S. B., Arends, D., McDaid, A. F., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.10.016 · Cited by 55 · see citing works

Functional signaling and gene regulatory networks between the oocyte and the surrounding cumulus cells

Biase, F. H., & Kimble, K. M. (2018) · BMC Genomics · doi:10.1186/s12864-018-4738-2 · Cited by 55 · see citing works

Maternal smoking impacts key biological pathways in newborns through epigenetic modification in Utero

Rotroff, D. M., Joubert, B. R., Marvel, S. W., Håberg, S. E., Wu, M. C., Nilsen, R. M., Ueland, P. M., Nystad, W., London, S. J., & Motsinger‐Reif, A. A. (2016) · BMC Genomics · doi:10.1186/s12864-016-3310-1 · Cited by 54 · see citing works

Genome-Wide Association Study of Meat Quality Traits in Hanwoo Beef Cattle Using Imputed Whole-Genome Sequence Data

Bedhane, M., van der Werf, J. H. J., Gondro, C., Duijvesteijn, N., Lim, D., Park, B., Park, M. N., Hee, R. S., & Clark, S. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01235 · Cited by 54 · see citing works

Exome sequencing in genetic disease: recent advances and considerations

Ross, J. P., Dion, P. A., & Rouleau, G. A. (2020) · F1000Research · doi:10.12688/f1000research.19444.1 · Cited by 54 · see citing works

Genetics and Brain Morphology

Strike, L. T., Couvy‐Duchesne, B., Hansell, N. K., Cuéllar-Partida, G., Medland, S. E., & Wright, M. J. (2015) · Neuropsychology Review · doi:10.1007/s11065-015-9281-1 · Cited by 53 · see citing works

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution

Yang, M., Huang, L., Huang, H., Tang, H., Zhang, N., Yang, H., Wu, J., & Mu, F. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac326 · Cited by 53 · see citing works

Association between genetic risk scoring for schizophrenia and bipolar disorder with regional subcortical volumes

Caseras, X., Tansey, K. E., Foley, S. F., & Linden, D. E. J. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.195 · Cited by 51 · see citing works

Identification of Elongated Primary Cilia with Impaired Mechanotransduction in Idiopathic Scoliosis Patients

Oliazadeh, N., Gorman, K. F., Eveleigh, R. J. M., Bourque, G., & Moreau, A. (2017) · Scientific Reports · doi:10.1038/srep44260 · Cited by 51 · see citing works

Comprehensive analysis of the major histocompatibility complex in systemic sclerosis identifies differential HLA associations by clinical and serological subtypes

Acosta‐Herrera, M., Kerick, M., López‐Isac, E., Assassi, S., Beretta, L., Simeón‐Aznar, C. P., Ortego‐Centeno, N., Proudman, S., Hunzelmann, N., Moroncini, G., et al. (2021) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2021-219884 · Cited by 50 · see citing works

Genome-wide studies to identify risk factors for kidney disease with a focus on patients with diabetes

Regele, F., Jelencsics, K. E., Shiffman, D., Paré, G., McQueen, M., Mann, J. F. E., & Oberbauer, R. W. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv087 · Cited by 49 · see citing works

Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines

Li, J. J., & Tong, X. (2020) · Patterns · doi:10.1016/j.patter.2020.100115 · Cited by 49 · see citing works

A frameshift mutation in ARMC3 is associated with a tail stump sperm defect in Swedish Red (Bos taurus) cattle

Pausch, H., Venhoranta, H., Wurmser, C., Hakala, K., Iso‐Touru, T., Sironen, A. I., Vingborg, R., Lohi, H., Söderquist, L., Fries, R., et al. (2016) · BMC Genetics · doi:10.1186/s12863-016-0356-7 · Cited by 48 · see citing works

Defining Phenotypes from Clinical Data to Drive Genomic Research

Robinson, J. R., Wei, W., Roden, D. M., & Denny, J. C. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013335 · Cited by 48 · see citing works

Genome-wide association studies for fatty acid metabolic traits in five divergent pig populations

Zhang, W., Yang, B., Zhang, J., Cui, L., Ma, J., Chen, C., Ai, H., Xiao, S., Ren, J., & Huang, L. (2016) · Scientific Reports · doi:10.1038/srep24718 · Cited by 48 · see citing works

From Common to Rare Variants: The Genetic Component of Alzheimer Disease

Nicolas, G., Charbonnier, C., & Campion, D. (2016) · Human Heredity · doi:10.1159/000452256 · Cited by 47 · see citing works

Significance Thresholds for Rare Variant Signals

Greenwood, C. M. T., Xu, C., & Ciampi, A. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2824-8_12 · Cited by 1 · see citing works

The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease

Moutsianas, L., Agarwala, V., Fuchsberger, C., Flannick, J. A., Rivas, M. A., Gaulton, K. J., Albers, P. K., McVean, G., Boehnke, M., Altshuler, D., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005165 · Cited by 129 · see citing works

Pooled Association Tests for Rare Genetic Variants: A Review and Some New Results

Derkach, A., Lawless, J. F., & Sun, L. (2014) · Statistical Science · doi:10.1214/13-sts456 · Cited by 78 · see citing works

Assessment of Pooled Association Tests for Rare Genetic Variants within a Unified FrameworkPreprint

Derkach, A., Lawless, J. F., & Sun, L. (2012) · arXiv (Cornell University) · Cited by 2 · see citing works

Power Analysis Provides Bounds for Genetic Architecture and Insights to Challenges for Rare Variant Association StudiesPreprint

Derkach, A., Zhang, H., & Chatterjee, N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/100891 · Cited by 1 · see citing works